no code implementations • 13 Apr 2023 • Leihang Zhang, Jiapeng Liu, Qiang Yan
However, these approaches suffer from the inability to select appropriate parameters and incomplete models that overlook the quantitative relation between words with topics and topics with text.
1 code implementation • 4 Mar 2023 • Yujie Lin, Chenyang Wang, Zhumin Chen, Zhaochun Ren, Xin Xin, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren
STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items.
1 code implementation • 4 Jan 2023 • Yujie Lin, Zhumin Chen, Zhaochun Ren, Chenyang Wang, Qiang Yan, Maarten de Rijke, Xiuzhen Cheng, Pengjie Ren
To address the limitation of sequential recommenders with side information, we define a way to fuse side information and alleviate the problem of missing side information by proposing a unified task, namely the missing information imputation (MII), which randomly masks some feature fields in a given sequence of items, including item IDs, and then forces a predictive model to recover them.
no code implementations • 6 Apr 2022 • Shanshan Wang, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Qiang Yan, Pengjie Ren
In this work, we propose a simple yet effective attention guiding mechanism to improve the performance of PLM by encouraging attention towards the established goals.
no code implementations • 26 Oct 2021 • Zhe Zhang, Shiyao Ma, Jiangtian Nie, Yi Wu, Qiang Yan, Xiaoke Xu, Dusit Niyato
In this paper, we present a robust semi-supervised FL system design, where the system aims to solve the problem of data availability and non-IID in FL.
1 code implementation • 29 Jun 2021 • Shanshan Wang, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Huasheng Liang, Qiang Yan, Evangelos Kanoulas, Maarten de Rijke
We seek to improve the performance for both frequent and rare ICD codes by using a contrastive graph-based EHR coding framework, CoGraph, which re-casts EHR coding as a few-shot learning task.
1 code implementation • 8 Jun 2021 • Shuyuan Zheng, Yang Cao, Masatoshi Yoshikawa, Huizhong Li, Qiang Yan
FL-Market decouples ML from the need to centrally gather training data on the broker's side using federated learning, an emerging privacy-preserving ML paradigm in which data owners collaboratively train an ML model by uploading local gradients (to be aggregated into a global gradient for model updating).
1 code implementation • 2 Apr 2020 • Yuzheng Li, Chuan Chen, Nan Liu, Huawei Huang, Zibin Zheng, Qiang Yan
To address these security issues, we proposed a decentralized federated learning framework based on blockchain, i. e., a Blockchain-based Federated Learning framework with Committee consensus (BFLC).
no code implementations • 29 Nov 2013 • Xudong Liu, Bing Xu, Yuyu Zhang, Qiang Yan, Liang Pang, Qiang Li, Hanxiao Sun, Bin Wang
The ICDM Challenge 2013 is to apply machine learning to the problem of hotel ranking, aiming to maximize purchases according to given hotel characteristics, location attractiveness of hotels, user's aggregated purchase history and competitive online travel agency information for each potential hotel choice.